Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan; Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan.
Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan.
Int J Med Inform. 2023 Oct;178:105176. doi: 10.1016/j.ijmedinf.2023.105176. Epub 2023 Aug 6.
Artificial intelligence (AI) holds significant potential to be a valuable tool in healthcare. However, its application for predicting bacteremia among adult febrile patients in the emergency department (ED) remains unclear. Therefore, we conducted a study to provide clarity on this issue.
Adult febrile ED patients with blood cultures at Chi Mei Medical Center were divided into derivation (January 2017 to June 2019) and validation groups (July 2019 to December 2020). The derivation group was utilized to develop AI models using twenty-one feature variables and five algorithms to predict bacteremia. The performance of these models was compared with qSOFA score. The AI model with the highest area under the receiver operating characteristics curve (AUC) was chosen to implement the AI prediction system and tested on the validation group.
The study included 5,647 febrile patients. In the derivation group, there were 3,369 patients with a mean age of 61.4 years, and 50.7% were female, including 508 (13.8%) with bacteremia. The model with the best AUC was built using the random forest algorithm (0.761), followed by logistic regression (0.755). All five models demonstrated better AUC than the qSOFA score (0.560). The random forest model was adopted to build a real-time AI prediction system integrated into the hospital information system, and the AUC achieved 0.709 in the validation group.
The AI model shows promise to predict bacteremia in adult febrile ED patients; however, further external validation in different hospitals and populations is necessary to verify its effectiveness.
人工智能（AI）在医疗保健领域具有很大的潜力，可以成为一种有价值的工具。然而，它在预测急诊科（ED）成人发热患者菌血症方面的应用仍不清楚。因此，我们进行了一项研究以阐明这个问题。
将奇美医疗中心的成人发热 ED 患者血培养分为推导组（2017 年 1 月至 2019 年 6 月）和验证组（2019 年 7 月至 2020 年 12 月）。推导组使用二十一个特征变量和五种算法来开发 AI 模型，以预测菌血症。比较这些模型与 qSOFA 评分的性能。选择具有最高受试者工作特征曲线（ROC）下面积（AUC）的 AI 模型来实现 AI 预测系统，并在验证组上进行测试。
研究纳入了 5647 例发热患者。推导组 3369 例，平均年龄 61.4 岁，女性占 50.7%，其中 508 例（13.8%）有菌血症。AUC 最高的模型是使用随机森林算法（0.761）构建的，其次是逻辑回归（0.755）。所有五个模型的 AUC 均优于 qSOFA 评分（0.560）。采用随机森林模型构建实时 AI 预测系统，集成到医院信息系统中，验证组 AUC 为 0.709。
AI 模型有望预测成人发热 ED 患者的菌血症；然而，需要在不同医院和人群中进行进一步的外部验证，以验证其有效性。
